#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
##
## Attaching package: 'vip'
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## vi
#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)
#install.packages("BayesFactor")
library(BayesFactor)
## Loading required package: coda
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## Loading required package: Matrix
## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
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#install.packages('locfit')
library(locfit)
## locfit 1.5-9.8 2023-06-11
#install.packages('ggplot2’)
library(ggplot2)
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library(dplyr)
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#install.packages('networkD3')
library(networkD3)
library(rstanarm)
## Loading required package: Rcpp
## This is rstanarm version 2.26.1
## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores())
library(see)
#install.packages('tidyverse')
library(tidyverse)
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#install.packages('caret')
library(caret)
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library(ISLR)
#install.packages('MCMCpack')
library(MCMCpack)
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## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
## ## Support provided by the U.S. National Science Foundation
## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
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library(TDAstats)
library(ks)
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## Recall
#install.packages('googledrive')
library(googledrive)
#install.packages('stringr')
library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
##Add Bayesian tests functions
#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 3000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
#for the moment we implement the sign test. Signedrank will follows
probLeft <- mean (diffVector < rope_min)
probRope <- mean (diffVector > rope_min & diffVector < rope_max)
probRight <- mean (diffVector > rope_max)
results = list ("probLeft"=probLeft, "probRope"=probRope,
"probRight"=probRight)
return (results)
}
##Create function to conduct Bayesian Signed Rank Test
BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 30000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
sampledWeights <- rdirichlet(samples,weights)
winLeft <- vector(length = samples)
winRope <- vector(length = samples)
winRight <- vector(length = samples)
for (rep in 1:samples){
currentWeights <- sampledWeights[rep,]
for (i in 1:length(currentWeights)){
for (j in 1:length(currentWeights)){
product= currentWeights[i] * currentWeights[j]
if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
winRight[rep] <- winRight[rep] + product
}
else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
winRope[rep] <- winRope[rep] + product
}
else {
winLeft[rep] <- winLeft[rep] + product
}
}
}
maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
winRight[rep] <- (winRight[rep]==maxWins)*1/winners
winRope[rep] <- (winRope[rep]==maxWins)*1/winners
winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
}
results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
"winRight"=mean(winRight) )
return (results)
}
#Create function to conduct the Bayesian Correlated t.test
#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.
#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
if (rope_max < rope_min){
stop("rope_max should be larger than rope_min")
}
delta <- mean(diff_a_b)
n <- length(diff_a_b)
df <- n-1
stdX <- sd(diff_a_b)
sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
p.left <- pt((rope_min - delta)/sp, df)
p.rope <- pt((rope_max - delta)/sp, df)-p.left
results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
return (results)
}
set.seed(16974)
###################################################5.60.5 ROPE Comparisons for Dissertation
##Random Forest Results
rf_dataset_av<-c(0.8572, 0.9205, 0.9796)
rf_pca.5.60.5_n1_av<-c(0.8992, 0.9078, 0.9944)
rf_pca.5.60.5_n2_av<-c(0.7492, 0.8872, 0.9820)
rf_pca.5.60.5_n3_av<-c(0.8068, 0.9377, 0.9399)
rf_pca.5.60.5_n4_av<-c(0.9482, 0.9743, 0.9424)
rf_pca.5.60.5_n5_av<-c(0.9931, NA, 0.9937)
rf_kde.5.60.5_n1_av<-c(0.8604, 0.9475, 0.9675)
rf_kde.5.60.5_n2_av<-c(0.8442, 0.9460, 0.9805)
rf_kde.5.60.5_n3_av<-c(0.8366, 0.9098, 0.9828)
rf_kde.5.60.5_n4_av<-c(0.8538, 0.8113, 0.9875)
rf_kde.5.60.5_n5_av<-c(0.8735, 0.7262, 0.9891)
######################## ROPE PCA
diff_rf_pca.5.60.5_n1_av<-rf_dataset_av - rf_pca.5.60.5_n1_av
bsr_diff_rf_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n1_av
## $winLeft
## [1] 0.6768
##
## $winRope
## [1] 0.2408667
##
## $winRight
## [1] 0.08233333
bsr_diff_rf_pca.5.60.5_n1_av_odds.left<-bsr_diff_rf_pca.5.60.5_n1_av $winLeft/bsr_diff_rf_pca.5.60.5_n1_av $winRight
bsr_diff_rf_pca.5.60.5_n1_av_odds.left
## [1] 8.220243
plot(rope(diff_rf_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_rf_pca.5.60.5_n2_av<-rf_dataset_av - rf_pca.5.60.5_n2_av
bsr_diff_rf_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1469667
##
## $winRight
## [1] 0.8530333
bsr_diff_rf_pca.5.60.5_n2_av_odds.left<-bsr_diff_rf_pca.5.60.5_n2_av $winLeft/bsr_diff_rf_pca.5.60.5_n2_av $winRight
bsr_diff_rf_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_rf_pca.5.60.5_n3_av<-rf_dataset_av - rf_pca.5.60.5_n3_av
bsr_diff_rf_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n3_av
## $winLeft
## [1] 0.06153333
##
## $winRope
## [1] 0.04936667
##
## $winRight
## [1] 0.8891
bsr_diff_rf_pca.5.60.5_n3_av_odds.left<-bsr_diff_rf_pca.5.60.5_n3_av $winLeft/bsr_diff_rf_pca.5.60.5_n3_av $winRight
bsr_diff_rf_pca.5.60.5_n3_av_odds.left
## [1] 0.06920856
plot(rope(diff_rf_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_rf_pca.5.60.5_n4_av<-rf_dataset_av - rf_pca.5.60.5_n4_av
bsr_diff_rf_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n4_av
## $winLeft
## [1] 0.8001667
##
## $winRope
## [1] 0.04673333
##
## $winRight
## [1] 0.1531
bsr_diff_rf_pca.5.60.5_n4_av_odds.left<-bsr_diff_rf_pca.5.60.5_n4_av $winLeft/bsr_diff_rf_pca.5.60.5_n4_av $winRight
bsr_diff_rf_pca.5.60.5_n4_av_odds.left
## [1] 5.226432
plot(rope(diff_rf_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_rf_pca.5.60.5_n5_av<-rf_dataset_av - rf_pca.5.60.5_n5_av
#bsr_diff_rf_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_rf_pca.5.60.5_n5_av
#bsr_diff_rf_pca.5.60.5_n5_av_odds.left<-bsr_diff_rf_pca.5.60.5_n5_av $winLeft/bsr_diff_rf_pca.5.60.5_n5_av $winRight
#bsr_diff_rf_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_rf_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_rf_kde.5.60.5_n1_av<-rf_dataset_av - rf_kde.5.60.5_n1_av
bsr_diff_rf_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n1_av
## $winLeft
## [1] 0.2577
##
## $winRope
## [1] 0.6879333
##
## $winRight
## [1] 0.05436667
bsr_diff_rf_kde.5.60.5_n1_av_odds.left<-bsr_diff_rf_kde.5.60.5_n1_av $winLeft/bsr_diff_rf_kde.5.60.5_n1_av $winRight
bsr_diff_rf_kde.5.60.5_n1_av_odds.left
## [1] 4.740037
plot(rope(diff_rf_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n2_av<-rf_dataset_av - rf_kde.5.60.5_n2_av
bsr_diff_rf_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n2_av
## $winLeft
## [1] 0.2571667
##
## $winRope
## [1] 0.6854333
##
## $winRight
## [1] 0.0574
bsr_diff_rf_kde.5.60.5_n2_av_odds.left<-bsr_diff_rf_kde.5.60.5_n2_av $winLeft/bsr_diff_rf_kde.5.60.5_n2_av $winRight
bsr_diff_rf_kde.5.60.5_n2_av_odds.left
## [1] 4.480256
plot(rope(diff_rf_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n3_av<-rf_dataset_av - rf_kde.5.60.5_n3_av
bsr_diff_rf_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5830667
##
## $winRight
## [1] 0.4169333
bsr_diff_rf_kde.5.60.5_n3_av_odds.left<-bsr_diff_rf_kde.5.60.5_n3_av $winLeft/bsr_diff_rf_kde.5.60.5_n3_av $winRight
bsr_diff_rf_kde.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n4_av<-rf_dataset_av - rf_kde.5.60.5_n4_av
bsr_diff_rf_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5809
##
## $winRight
## [1] 0.4191
bsr_diff_rf_kde.5.60.5_n4_av_odds.left<-bsr_diff_rf_kde.5.60.5_n4_av $winLeft/bsr_diff_rf_kde.5.60.5_n4_av $winRight
bsr_diff_rf_kde.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n5_av<-rf_dataset_av - rf_kde.5.60.5_n5_av
bsr_diff_rf_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n5_av
## $winLeft
## [1] 0.2176
##
## $winRope
## [1] 0.2814333
##
## $winRight
## [1] 0.5009667
bsr_diff_rf_kde.5.60.5_n5_av_odds.left<-bsr_diff_rf_kde.5.60.5_n5_av $winLeft/bsr_diff_rf_kde.5.60.5_n5_av $winRight
bsr_diff_rf_kde.5.60.5_n5_av_odds.left
## [1] 0.4343602
plot(rope(diff_rf_kde.5.60.5_n5_av,c(-0.01,0.01)))
################################ Support Vector Machine
##Support Vector Machine Results
svm_dataset_av<-c(0.8234, 0.9312, 0.9792)
svm_pca.5.60.5_n1_av<-c(0.7062, 0.9146, 0.9943)
svm_pca.5.60.5_n2_av<-c(0.7093, 0.8937, 0.9817)
svm_pca.5.60.5_n3_av<-c(0.7670, 0.8040, 0.9410)
svm_pca.5.60.5_n4_av<-c(0.9364, 0.9844, 0.9367)
svm_pca.5.60.5_n5_av<-c(0.9921, NA, 0.9937)
svm_kde.5.60.5_n1_av<-c(0.8159, 0.9528, 0.9881)
svm_kde.5.60.5_n2_av<-c(0.8058, 0.9487, 0.9799)
svm_kde.5.60.5_n3_av<-c(0.8053, 0.6117, 0.9836)
svm_kde.5.60.5_n4_av<-c(0.8331, 0.8181, 0.9881)
svm_kde.5.60.5_n5_av<-c(0.8035, 0.6499, 0.9892)
######################## ROPE PCA
diff_svm_pca.5.60.5_n1_av<-svm_dataset_av - svm_pca.5.60.5_n1_av
bsr_diff_svm_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n1_av
## $winLeft
## [1] 0.07926667
##
## $winRope
## [1] 0.2403
##
## $winRight
## [1] 0.6804333
bsr_diff_svm_pca.5.60.5_n1_av_odds.left<-bsr_diff_svm_pca.5.60.5_n1_av$winLeft/bsr_diff_svm_pca.5.60.5_n1_av $winRight
bsr_diff_svm_pca.5.60.5_n1_av_odds.left
## [1] 0.1164944
plot(rope(diff_svm_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_svm_pca.5.60.5_n2_av<-svm_dataset_av - svm_pca.5.60.5_n2_av
bsr_diff_svm_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1439
##
## $winRight
## [1] 0.8561
bsr_diff_svm_pca.5.60.5_n2_av_odds.left<-bsr_diff_svm_pca.5.60.5_n2_av$winLeft/bsr_diff_svm_pca.5.60.5_n2_av $winRight
bsr_diff_svm_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_svm_pca.5.60.5_n3_av<-svm_dataset_av - svm_pca.5.60.5_n3_av
bsr_diff_svm_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008933333
##
## $winRight
## [1] 0.9910667
bsr_diff_svm_pca.5.60.5_n3_av_odds.left<-bsr_diff_svm_pca.5.60.5_n3_av$winLeft/bsr_diff_svm_pca.5.60.5_n3_av $winRight
bsr_diff_svm_pca.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_svm_pca.5.60.5_n4_av<-svm_dataset_av - svm_pca.5.60.5_n4_av
bsr_diff_svm_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n4_av
## $winLeft
## [1] 0.7988
##
## $winRope
## [1] 0.04706667
##
## $winRight
## [1] 0.1541333
bsr_diff_svm_pca.5.60.5_n4_av_odds.left<-bsr_diff_svm_pca.5.60.5_n4_av$winLeft/bsr_diff_svm_pca.5.60.5_n4_av $winRight
bsr_diff_svm_pca.5.60.5_n4_av_odds.left
## [1] 5.182526
plot(rope(diff_svm_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_svm_pca.5.60.5_n5_av<-svm_dataset_av - svm_pca.5.60.5_n5_av
#bsr_diff_svm_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_svm_pca.5.60.5_n5_av
#bsr_diff_svm_pca.5.60.5_n5_av_odds.left<-bsr_diff_svm_pca.5.60.5_n5_av$winLeft/bsr_diff_svm_pca.5.60.5_n5_av $winRight
#bsr_diff_svm_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_svm_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_svm_kde.5.60.5_n1_av<-svm_dataset_av - svm_kde.5.60.5_n1_av
bsr_diff_svm_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n1_av
## $winLeft
## [1] 0.2344
##
## $winRope
## [1] 0.7656
##
## $winRight
## [1] 0
bsr_diff_svm_kde.5.60.5_n1_av_odds.left<-bsr_diff_svm_kde.5.60.5_n1_av $winLeft/bsr_diff_svm_kde.5.60.5_n1_av $winRight
bsr_diff_svm_kde.5.60.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_svm_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n2_av<-svm_dataset_av - svm_kde.5.60.5_n2_av
bsr_diff_svm_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n2_av
## $winLeft
## [1] 0.04986667
##
## $winRope
## [1] 0.8991667
##
## $winRight
## [1] 0.05096667
bsr_diff_svm_kde.5.60.5_n2_av_odds.left<-bsr_diff_svm_kde.5.60.5_n2_av $winLeft/bsr_diff_svm_kde.5.60.5_n2_av $winRight
bsr_diff_svm_kde.5.60.5_n2_av_odds.left
## [1] 0.9784173
plot(rope(diff_svm_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n3_av<-svm_dataset_av - svm_kde.5.60.5_n3_av
bsr_diff_svm_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.3941667
##
## $winRight
## [1] 0.6058333
bsr_diff_swm_kde.5.60.5_n3_av_odds.left<-bsr_diff_svm_kde.5.60.5_n3_av$winLeft/bsr_diff_svm_kde.5.60.5_n3_av$winRight
bsr_diff_swm_kde.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n4_av<-svm_dataset_av - svm_kde.5.60.5_n4_av
bsr_diff_svm_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5854
##
## $winRight
## [1] 0.4146
bsr_diff_svm_kde.5.60.5_n4_av_odds.left<-bsr_diff_svm_kde.5.60.5_n4_av $winLeft/bsr_diff_svm_kde.5.60.5_n4_av $winRight
bsr_diff_svm_kde.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n5_av<-svm_dataset_av - svm_kde.5.60.5_n5_av
bsr_diff_svm_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n5_av
## $winLeft
## [1] 0.07706667
##
## $winRope
## [1] 0.2435667
##
## $winRight
## [1] 0.6793667
bsr_diff_svm_kde.5.60.5_n5_av_odds.left<-bsr_diff_svm_kde.5.60.5_n5_av $winLeft/bsr_diff_svm_kde.5.60.5_n5_av $winRight
bsr_diff_svm_kde.5.60.5_n5_av_odds.left
## [1] 0.113439
plot(rope(diff_svm_kde.5.60.5_n5_av,c(-0.01,0.01)))
######################### Neural Network
##Neural Network Results
nn1_dataset_av<-c(0.8198, 0.4913, 0.9799)
nn1_pca.5.60.5_n1_av<-c(0.9009, 0.8828, 0.9943)
nn1_pca.5.60.5_n2_av<-c(0.6780, 0.6380, 0.9823)
nn1_pca.5.60.5_n3_av<-c(0.7816, 0.6827, 0.9385)
nn1_pca.5.60.5_n4_av<-c(0.9456, 0.9542, 0.9381)
nn1_pca.5.60.5_n5_av<-c(0.9930, NA, 0.9937)
nn1_kde.5.60.5_n1_av<-c(0.8250, 0.6145, 0.9690)
nn1_kde.5.60.5_n2_av<-c(0.8250, 0.7469, 0.9835)
nn1_kde.5.60.5_n3_av<-c(0.8126, 0.6686, 0.9841)
nn1_kde.5.60.5_n4_av<-c(0.8426, 0.7828, 0.9881)
nn1_kde.5.60.5_n5_av<-c(0.8685, 0.6681, 0.9891)
######################## ROPE PCA
diff_nn1_pca.5.60.5_n1_av<-nn1_dataset_av - nn1_pca.5.60.5_n1_av
bsr_diff_nn1_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n1_av
## $winLeft
## [1] 0.9637
##
## $winRope
## [1] 0.0363
##
## $winRight
## [1] 0
bsr_diff_nn1_pca.5.60.5_n1_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n1_av$winLeft/bsr_diff_nn1_pca.5.60.5_n1_av $winRight
bsr_diff_nn1_pca.5.60.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_nn1_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n2_av<-nn1_dataset_av - nn1_pca.5.60.5_n2_av
bsr_diff_nn1_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n2_av
## $winLeft
## [1] 0.3514667
##
## $winRope
## [1] 0.3035333
##
## $winRight
## [1] 0.345
bsr_diff_nn1_pca.5.60.5_n2_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n2_av$winLeft/bsr_diff_nn1_pca.5.60.5_n2_av $winRight
bsr_diff_nn1_pca.5.60.5_n2_av_odds.left
## [1] 1.018744
plot(rope(diff_nn1_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n3_av<-nn1_dataset_av - nn1_pca.5.60.5_n3_av
bsr_diff_nn1_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n3_av
## $winLeft
## [1] 0.4442
##
## $winRope
## [1] 0.016
##
## $winRight
## [1] 0.5398
bsr_diff_nn1_pca.5.60.5_n3_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n3_av$winLeft/bsr_diff_nn1_pca.5.60.5_n3_av $winRight
bsr_diff_nn1_pca.5.60.5_n3_av_odds.left
## [1] 0.8228974
plot(rope(diff_nn1_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n4_av<-nn1_dataset_av - nn1_pca.5.60.5_n4_av
bsr_diff_nn1_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n4_av
## $winLeft
## [1] 0.8772333
##
## $winRope
## [1] 0.01606667
##
## $winRight
## [1] 0.1067
bsr_diff_nn1_pca.5.60.5_n4_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n4_av$winLeft/bsr_diff_nn1_pca.5.60.5_n4_av $winRight
bsr_diff_nn1_pca.5.60.5_n4_av_odds.left
## [1] 8.221493
plot(rope(diff_nn1_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_nn1_pca.5.60.5_n5_av<-nn1_dataset_av - nn1_pca.5.60.5_n5_av
#bsr_diff_nn1_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_nn1_pca.5.60.5_n5_av
#bsr_diff_nn1_pca.5.60.5_n5_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n5_av$winLeft/bsr_diff_nn1_pca.5.60.5_n5_av $winRight
#bsr_diff_nn1_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_nn1_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_nn1_kde.5.60.5_n1_av<-nn1_dataset_av - nn1_kde.5.60.5_n1_av
bsr_diff_nn1_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n1_av
## $winLeft
## [1] 0.4724333
##
## $winRope
## [1] 0.4514667
##
## $winRight
## [1] 0.0761
bsr_diff_nn1_kde.5.60.5_n1_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n1_av $winLeft/bsr_diff_nn1_kde.5.60.5_n1_av $winRight
bsr_diff_nn1_kde.5.60.5_n1_av_odds.left
## [1] 6.20806
plot(rope(diff_nn1_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n2_av<-nn1_dataset_av - nn1_kde.5.60.5_n2_av
bsr_diff_nn1_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n2_av
## $winLeft
## [1] 0.4248333
##
## $winRope
## [1] 0.5751667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n2_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n2_av $winLeft/bsr_diff_nn1_kde.5.60.5_n2_av $winRight
bsr_diff_nn1_kde.5.60.5_n2_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n3_av<-nn1_dataset_av - nn1_kde.5.60.5_n3_av
bsr_diff_nn1_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n3_av
## $winLeft
## [1] 0.4201667
##
## $winRope
## [1] 0.5798333
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n3_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n3_av $winLeft/bsr_diff_nn1_kde.5.60.5_n3_av $winRight
bsr_diff_nn1_kde.5.60.5_n3_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n4_av<-nn1_dataset_av - nn1_kde.5.60.5_n4_av
bsr_diff_nn1_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n4_av
## $winLeft
## [1] 0.8548333
##
## $winRope
## [1] 0.1451667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n4_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n4_av $winLeft/bsr_diff_nn1_kde.5.60.5_n4_av $winRight
bsr_diff_nn1_kde.5.60.5_n4_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n5_av<-nn1_dataset_av - nn1_kde.5.60.5_n5_av
bsr_diff_nn1_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n5_av
## $winLeft
## [1] 0.8539333
##
## $winRope
## [1] 0.1460667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n5_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n5_av $winLeft/bsr_diff_nn1_kde.5.60.5_n5_av $winRight
bsr_diff_nn1_kde.5.60.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n5_av,c(-0.01,0.01)))
################################ Logistic Regression
##Logistic Regression Results
lr_dataset_av<-c(0.8511, 0.9262, 0.9793)
lr_pca.5.60.5_n1_av<-c(0.8459, 0.9093, 0.9943)
lr_pca.5.60.5_n2_av<-c(0.7418, 0.8885, 0.9809)
lr_pca.5.60.5_n3_av<-c(0.7859, 0.9453, 0.9334)
lr_pca.5.60.5_n4_av<-c(0.9419, 0.9866, 0.9452)
lr_pca.5.60.5_n5_av<-c(0.9792, NA, 0.9924)
lr_kde.5.60.5_n1_av<-c(0.953, 0.572, 0.9665)
lr_kde.5.60.5_n2_av<-c(0.947, 0.746, 0.9801)
lr_kde.5.60.5_n3_av<-c(0.917, 0.876, 0.9842)
lr_kde.5.60.5_n4_av<-c(0.827, 0.788, 0.9885)
lr_kde.5.60.5_n5_av<-c(0.617, 0.740, 0.9892)
######################## ROPE PCA
diff_lr_pca.5.60.5_n1_av<-lr_dataset_av - lr_pca.5.60.5_n1_av
bsr_diff_lr_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n1_av
## $winLeft
## [1] 0.0555
##
## $winRope
## [1] 0.7729
##
## $winRight
## [1] 0.1716
bsr_diff_lr_pca.5.60.5_n1_av_odds.left<-bsr_diff_lr_pca.5.60.5_n1_av$winLeft/bsr_diff_lr_pca.5.60.5_n1_av $winRight
bsr_diff_lr_pca.5.60.5_n1_av_odds.left
## [1] 0.3234266
plot(rope(diff_lr_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_lr_pca.5.60.5_n2_av<-lr_dataset_av - lr_pca.5.60.5_n2_av
bsr_diff_lr_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1431333
##
## $winRight
## [1] 0.8568667
bsr_diff_lr_pca.5.60.5_n2_av_odds.left<-bsr_diff_lr_pca.5.60.5_n2_av$winLeft/bsr_diff_lr_pca.5.60.5_n2_av $winRight
bsr_diff_lr_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_lr_pca.5.60.5_n3_av<-lr_dataset_av - lr_pca.5.60.5_n3_av
bsr_diff_lr_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n3_av
## $winLeft
## [1] 0.0609
##
## $winRope
## [1] 0.0509
##
## $winRight
## [1] 0.8882
bsr_diff_lr_pca.5.60.5_n3_av_odds.left<-bsr_diff_lr_pca.5.60.5_n3_av$winLeft/bsr_diff_lr_pca.5.60.5_n3_av $winRight
bsr_diff_lr_pca.5.60.5_n3_av_odds.left
## [1] 0.06856564
plot(rope(diff_lr_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_lr_pca.5.60.5_n4_av<-lr_dataset_av - lr_pca.5.60.5_n4_av
bsr_diff_lr_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n4_av
## $winLeft
## [1] 0.8745333
##
## $winRope
## [1] 0.01456667
##
## $winRight
## [1] 0.1109
bsr_diff_lr_pca.5.60.5_n4_av_odds.left<-bsr_diff_lr_pca.5.60.5_n4_av$winLeft/bsr_diff_lr_pca.5.60.5_n4_av $winRight
bsr_diff_lr_pca.5.60.5_n4_av_odds.left
## [1] 7.885783
plot(rope(diff_lr_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_lr_pca.5.60.5_n5_av<-lr_dataset_av - lr_pca.5.60.5_n5_av
#bsr_diff_lr_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_lr_pca.5.60.5_n5_av
#bsr_diff_lr_pca.5.60.5_n5_av_odds.left<-bsr_diff_lr_pca.5.60.5_n5_av$winLeft/bsr_diff_lr_pca.5.60.5_n5_av $winRight
#bsr_diff_lr_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_lr_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_lr_kde.5.60.5_n1_av<-lr_dataset_av - lr_kde.5.60.5_n1_av
bsr_diff_lr_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n1_av
## $winLeft
## [1] 0.2779333
##
## $winRope
## [1] 0.0601
##
## $winRight
## [1] 0.6619667
bsr_diff_lr_kde.5.60.5_n1_av_odds.left<-bsr_diff_lr_kde.5.60.5_n1_av $winLeft/bsr_diff_lr_kde.5.60.5_n1_av $winRight
bsr_diff_lr_kde.5.60.5_n1_av_odds.left
## [1] 0.41986
plot(rope(diff_lr_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n2_av<-lr_dataset_av - lr_kde.5.60.5_n2_av
bsr_diff_lr_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n2_av
## $winLeft
## [1] 0.3021
##
## $winRope
## [1] 0.1983
##
## $winRight
## [1] 0.4996
bsr_diff_lr_kde.5.60.5_n2_av_odds.left<-bsr_diff_lr_kde.5.60.5_n2_av $winLeft/bsr_diff_lr_kde.5.60.5_n2_av $winRight
bsr_diff_lr_kde.5.60.5_n2_av_odds.left
## [1] 0.6046837
plot(rope(diff_lr_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n3_av<-lr_dataset_av - lr_kde.5.60.5_n3_av
bsr_diff_lr_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n3_av
## $winLeft
## [1] 0.3459667
##
## $winRope
## [1] 0.3117
##
## $winRight
## [1] 0.3423333
bsr_diff_lr_kde.5.60.5_n3_av_odds.left<-bsr_diff_lr_kde.5.60.5_n3_av $winLeft/bsr_diff_lr_kde.5.60.5_n3_av $winRight
bsr_diff_lr_kde.5.60.5_n3_av_odds.left
## [1] 1.010613
plot(rope(diff_lr_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n4_av<-lr_dataset_av - lr_kde.5.60.5_n4_av
bsr_diff_lr_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.294
##
## $winRight
## [1] 0.706
bsr_diff_lr_kde.5.60.5_n4_av_odds.left<-bsr_diff_lr_kde.5.60.5_n4_av $winLeft/bsr_diff_lr_kde.5.60.5_n4_av $winRight
bsr_diff_lr_kde.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n5_av<-lr_dataset_av - lr_kde.5.60.5_n5_av
bsr_diff_lr_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n5_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1454
##
## $winRight
## [1] 0.8546
bsr_diff_lr_kde.5.60.5_n5_av_odds.left<-bsr_diff_lr_kde.5.60.5_n5_av $winLeft/bsr_diff_lr_kde.5.60.5_n5_av $winRight
bsr_diff_lr_kde.5.60.5_n5_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n5_av,c(-0.01,0.01)))
#################################################### Naive Bayes
##Naive Bayes Results
nb_dataset_av<-c(0.7649, 0.9028, 0.9627)
nb_pca.5.60.5_n1_av<-c(0.8948, 0.8348, 0.9900)
nb_pca.5.60.5_n2_av<-c(0.5163, 0.8589, 0.9543)
#nb_pca.5.60.5_n3_av<-c(0.7447, NA, 0.9236)
nb_pca.5.60.5_n4_av<-c(0.9351, 0.9743, 0.9033)
#nb_pca.5.60.5_n5_av<-c(0.9921, NA, NA)
nb_kde.5.60.5_n1_av<-c(0.7507, 0.9162, 0.9524)
nb_kde.5.60.5_n2_av<-c(0.5231, 0.8586, 0.9536)
#nb_kde.5.60.5_n3_av<-c(0.7444, NA, 0.9234)
nb_kde.5.60.5_n4_av<-c(0.9351, 0.9687, 0.9054)
#nb_kde.5.60.5_n5_av<-c(0.9921, NA, NA)
######################## ROPE PCA
diff_nb_pca.5.60.5_n1_av<-nb_dataset_av - nb_pca.5.60.5_n1_av
bsr_diff_nb_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nb_pca.5.60.5_n1_av
## $winLeft
## [1] 0.7235333
##
## $winRope
## [1] 0.01703333
##
## $winRight
## [1] 0.2594333
bsr_diff_nb_pca.5.60.5_n1_av_odds.left<-bsr_diff_nb_pca.5.60.5_n1_av$winLeft/bsr_diff_nb_pca.5.60.5_n1_av $winRight
bsr_diff_nb_pca.5.60.5_n1_av_odds.left
## [1] 2.788899
plot(rope(diff_nb_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_nb_pca.5.60.5_n2_av<-nb_dataset_av - nb_pca.5.60.5_n2_av
bsr_diff_nb_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nb_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1427667
##
## $winRight
## [1] 0.8572333
bsr_diff_nb_pca.5.60.5_n2_av_odds.left<-bsr_diff_nb_pca.5.60.5_n2_av$winLeft/bsr_diff_nb_pca.5.60.5_n2_av $winRight
bsr_diff_nb_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nb_pca.5.60.5_n2_av,c(-0.01,0.01)))
#diff_nb_pca.5.60.5_n3_av<-nb_dataset_av - nb_pca.5.60.5_n3_av
#bsr_diff_nb_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n3_av),-0.01,0.01)
#bsr_diff_nb_pca.5.60.5_n3_av
#bsr_diff_nb_pca.5.60.5_n3_av_odds.left<-bsr_diff_nb_pca.5.60.5_n3_av$winLeft/bsr_diff_nb_pca.5.60.5_n3_av $winRight
#bsr_diff_nb_pca.5.60.5_n3_av_odds.left
#plot(rope(diff_nb_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_nb_pca.5.60.5_n4_av<-nb_dataset_av - nb_pca.5.60.5_n4_av
bsr_diff_nb_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nb_pca.5.60.5_n4_av
## $winLeft
## [1] 0.8001
##
## $winRope
## [1] 0.04746667
##
## $winRight
## [1] 0.1524333
bsr_diff_nb_pca.5.60.5_n4_av_odds.left<-bsr_diff_nb_pca.5.60.5_n4_av$winLeft/bsr_diff_nb_pca.5.60.5_n4_av $winRight
bsr_diff_nb_pca.5.60.5_n4_av_odds.left
## [1] 5.248852
plot(rope(diff_nb_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_nb_pca.5.60.5_n5_av<-nb_dataset_av - nb_pca.5.60.5_n5_av
#bsr_diff_nb_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_nb_pca.5.60.5_n5_av
#bsr_diff_nb_pca.5.60.5_n5_av_odds.left<-bsr_diff_nb_pca.5.60.5_n5_av$winLeft/bsr_diff_nb_pca.5.60.5_n5_av $winRight
#bsr_diff_nb_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_nb_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_nb_kde.5.60.5_n1_av<-nb_dataset_av - nb_kde.5.60.5_n1_av
bsr_diff_nb_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nb_kde.5.60.5_n1_av
## $winLeft
## [1] 0.05526667
##
## $winRope
## [1] 0.5812333
##
## $winRight
## [1] 0.3635
bsr_diff_nb_kde.5.60.5_n1_av_odds.left<-bsr_diff_nb_kde.5.60.5_n1_av $winLeft/bsr_diff_nb_kde.5.60.5_n1_av $winRight
bsr_diff_nb_kde.5.60.5_n1_av_odds.left
## [1] 0.1520403
plot(rope(diff_nb_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_nb_kde.5.60.5_n2_av<-nb_dataset_av - nb_kde.5.60.5_n2_av
bsr_diff_nb_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nb_kde.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1449
##
## $winRight
## [1] 0.8551
bsr_diff_nb_kde.5.60.5_n2_av_odds.left<-bsr_diff_nb_kde.5.60.5_n2_av $winLeft/bsr_diff_nb_kde.5.60.5_n2_av $winRight
bsr_diff_nb_kde.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nb_kde.5.60.5_n2_av,c(-0.01,0.01)))
#diff_nb_kde.5.60.5_n3_av<-nb_dataset_av - nb_kde.5.60.5_n3_av
#bsr_diff_nb_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n3_av),-0.01,0.01)
#bsr_diff_nb_kde.5.60.5_n3_av
#bsr_diff_nb_kde.5.60.5_n3_av_odds.left<-bsr_diff_nb_kde.5.60.5_n3_av $winLeft/bsr_diff_nb_kde.5.60.5_n3_av #$winRight
#bsr_diff_nb_kde.5.60.5_n3_av_odds.left
#plot(rope(diff_nb_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_nb_kde.5.60.5_n4_av<-nb_dataset_av - nb_kde.5.60.5_n4_av
bsr_diff_nb_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nb_kde.5.60.5_n4_av
## $winLeft
## [1] 0.8
##
## $winRope
## [1] 0.0461
##
## $winRight
## [1] 0.1539
bsr_diff_nb_kde.5.60.5_n4_av_odds.left<-bsr_diff_nb_kde.5.60.5_n4_av $winLeft/bsr_diff_nb_kde.5.60.5_n4_av $winRight
bsr_diff_nb_kde.5.60.5_n4_av_odds.left
## [1] 5.198181
plot(rope(diff_nb_kde.5.60.5_n4_av,c(-0.01,0.01)))
#diff_nb_kde.5.60.5_n5_av<-nb_dataset_av - nb_kde.5.60.5_n5_av
#bsr_diff_nb_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_nb_kde.5.60.5_n5_av
#bsr_diff_nb_kde.5.60.5_n5_av_odds.left<-bsr_diff_nb_kde.5.60.5_n5_av $winLeft/bsr_diff_nb_kde.5.60.5_n5_av #$winRight
#bsr_diff_nb_kde.5.60.5_n5_av_odds.left
#plot(rope(diff_nb_kde.5.60.5_n5_av,c(-0.01,0.01)))